Learning 1-Bit LiDAR-based Localization with Auxiliary Objective

6-DoF LiDAR-based localization is a fundamental capabilityfor autonomous systems operating in large-scale outdoor environments.Many deep-learning-based localization methods have achieved promisingperformance so far. However, as one of the always-on modules competingfor limited on-board computational resources, the localization module isexpected to consume only a small portion of the overall compute bud-get. Most existing learning-based methods are still too heavy for thispurpose. In contrast, binary neural networks (BNNs) offer an appealingsolution, but the 1-bit compression causes severe information loss andperformance drop. In this paper, we address this challenge by propos-ing Binarized LiDAR-based Localization (BiLoc), the first binary neuralnetwork framework for 6-DoF LiDAR localization. Specifically, we rein-terpret the training of BNNs from the perspective of the information-bottleneck principle, aiming at retaining minimal yet sufficient represen-tations for pose estimation while suppressing redundant variations. Andwe introduce an auxiliary objective that adaptively regulates informationretention in the binary encoder, effectively mitigating the informationloss caused by binarization. This auxiliary objective provides additionaloptimization signals that compensate for the limited representationalcapacity and the gradient mismatch inherent in BNNs. Extensive exper-iments on large-scale outdoor LiDAR datasets demonstrate that BiLocestablishes a new state of the art for LiDAR localization with BNNs.